Kriging-Based Variable Screening Method for Aircraft Optimization Problems with Expensive Functions by Yadong Wang, Xinyao Duan, Jiang Wang, Jin Guo, Minglei Han

“…A genetic algorithm (GA) is employed to achieve the global optimum of the log-likelihood function. …”
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A machine learning based prediction model for short term efficacy of nasopharyngeal carcinoma by Qiulu Zhong, Xiangde Li, Qinghua Du, Qianfu Liang, Danjing Luo, Jiaying Wen, Haiying Yue, Wenqi Liu, Xiaodong Zhu, Jian Li

“…Three machine learning algorithms were used to construct predictive models for the short-term efficacy of LANPC. …”
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Development and validation of a predictive model for new HIV infection screening among persons 15 years and above in primary healthcare settings in Kenya: a study protocol by Simon Karanja, Amos Otieno Olwendo, Gideon Kikuvi

“…Introduction This study seeks to determine incidence, comorbidities and drivers for new HIV infections to develop, test and validate a risk prediction model for screening for new cases of HIV.Methods and analysis The study has two components: a cross-sectional study to develop the prediction model using the HIV dataset from the Kenya AIDS and STI Control Programme and a 15-month prospective study for the validation of the model. …”
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Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: a systematic review an... by Lies Dina Liastuti, Lies Dina Liastuti, Yosilia Nursakina, Yosilia Nursakina

“…Most studies utilized deep learning models using either ultrasound or echocardiographic images. …”
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Research on the optimization model of anti-breast cancer candidate drugs based on machine learning by Zhou Dong, Hong Chen, Yuchen Yang, Hairong Hao

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Research on Bearing Fault Diagnosis Method Based on MESO-TCN by Ruibin Gao, Jing Zhu, Yifan Wu, Kaiwen Xiao, Yang Shen

“…The method integrates feature filtering, network modeling, and parameter optimization into a unified diagnostic framework. …”
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Unlocking The Potential of Hybrid Models for Prognostic Biomarker Discovery in Oral Cancer Survival Analysis: A Retrospective Cohort Study by Leila Nezamabadi Farahani, Anoshirvan Kazemnejad, Mahlagha Afrasiabi, Leili Tapak

“…Concordance index (C-index), mean absolute error (MAE), mean squared error (MSE) and R-squares, were used to evaluate the performance of the models using selected features. Functional enrichment analysis was performed using DAVID database, and external validation utilized three independent datasets (GSE9844, GSE75538, GSE37991, GSE42743).Results: The findings indicated that the PSO-based method outperformed the GA-based method, achieving a smaller MAE (0.061) and MSE (0.005), R-square (0.99) and C-index (0.973), selecting 291 probes from 1069 screened. …”
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Molecular Docking: Methodological Approaches of Risk Assessment by A. Kh. Taldaev, I. D. Nikitin, R. P. Terekhov, I. A. Selivanova

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Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles by Yiling Ye, Xiaowen Zhuang, Cai Yi, Dinggao Liu, Zhenpeng Tang

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Identification of immune-related biomarkers linked to systemic lupus erythematosus and dilated cardiomyopathy through integrated bioinformatics analysis and multiple machine learni... by Gaijie Li, Liwen Lin, Shushu Wang, Kachun Lu, KaMan Szeto, Guiting Zhou, Xianwen Tang, Chuanjin Luo, Chuanjin Luo

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New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs by Patrycja S. Matusik, Zbisław Tabor, Iwona Kucybała, Jarosław D. Jarczewski, Tadeusz J. Popiela

“…Conclusion: The use of AI may optimize the screening process for cardiomegaly on CXRs. Future studies should focus on improving the accuracy of AI algorithms and on assessing the usefulness both of CTR and TCD measurements in screening for cardiomegaly.…”
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Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets by Kuoyuan Cheng, Laura Martin‐Sancho, Lipika R Pal, Yuan Pu, Laura Riva, Xin Yin, Sanju Sinha, Nishanth Ulhas Nair, Sumit K Chanda, Eytan Ruppin

“…Abstract Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. …”
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An Emotion-Driven Vocal Biomarker-Based PTSD Screening Tool by Thomas F. Quatieri, Jing Wang, James R. Williamson, Richard DeLaura, Tanya Talkar, Nancy P. Solomon, Stefanie E. Kuchinsky, Megan Eitel, Tracey Brickell, Sara Lippa, Kristin J. Heaton, Douglas S. Brungart, Louis French, Rael Lange, Jeffrey Palmer, Hayley Reynolds

“…<italic>Results:</italic> Speech from low-arousal and positive-valence regions provide the highest discrimination for PTSD. Our model achieved an AUC (area under the curve) of 0.80 in detecting PCL-C ratings, outperforming models with no emotion filtering (AUC &#x003D; 0.68). …”
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Building an Otoscopic screening prototype tool using deep learning by Devon Livingstone, Aron S. Talai, Justin Chau, Nils D. Forkert

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Construction of a predictive model for cognitive impairment among older adults in Northwest China by Yu Wang, Ni Wang, Yanjie Zhao, Xiaoyan Wang, Yuqin Nie, Liping Ding

“…Model performance was evaluated on the basis of the area under the curve, sensitivity, specificity, accuracy, F1 score, precision, and recall.ResultsA total of 12,332 older adults were recruited and screened with the Mini-Mental State Examination Scale. …”
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The Effect of Extended Smartphone Screen Time on Continuous Partial Attention by M. Fırat

“…Students attributed this finding to hypnotic algorithms, distracting redundancy, marketing and advertising, passive receiver mode, short video flow, and surprising content. …”
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ALGEBRAIC MODELS OF STRIP LINES IN A MULTILAYER DIELECTRIC MEDIUM by A. N. Kovalenko, A. N. Zhukov

“…The use of the Chebyshev basis and the improvement of the series convergence made it possible to develop an effective algorithm for calculating the basic electrodynamic parameters of the strip lines - the propagation constants and the wave impedances of the natural waves. …”
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Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics by ChenDeng Lao, Wei Wei, JianWen Cheng, ShiJie Liao, XiaoLin Luo, Qian Huang, HengZhen Huang, JinMin Zhao

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Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning by HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi

“…As a branch of artificial intelligence, machine learning algorithms have demonstrated exceptional capabilities in processing large-scale data, feature extraction, and model optimization, leading to their increasing application in the screening of antimicrobial substances. …”
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Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings by Amber Ahmad, Sahil Ahmad, Rida Ahmad, Jahnavi Bodi, Abdulla Mohamed, Ahmad Wasim

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